Research On Multi-layer Immune Model And Its Application In Fault Diagnosis

We are constantly being exposed to infectious agents and yet, in most cases, we are able to resist these infections. It is our immune system that enables us to resist infections. The natural immune system is a complex multi-layer defense system, with its cell diversity, anomaly detection, interactive, Life-long learning and memory of information processing characteristics. Inspired by theoretical immunology, in this study, we explore the design and application of fault diagnosis system based on artificial immune systems (AISs) for electromechanical device.This thesis examines multi-layered defense structure of Biology immune system, especially on innate immunity system, adaptive immunity system and interaction between them. The importance of the innate immune system suggests that AISs should also incorporate models of innate and adaptive immune system to structure diagnosis model with multifold detection mechanisms and improve the performance of the approach.Inspired by the multi-layer defense mechanism and incorporates the feedback mechanism in the nature immune system, the paper proposes a multi-layer immune model for fault diagnosis (MIFD) which incorporate both innate and adaptive immune system mechanisms on the purpose of enhancing the performance of the artificial immune system. In the multi-layer model, the innate immune layer directs recognition of known fault that could not cause influence to other nodes, the propagation immune layer adopts the structure of the B-lymphocyte network to construct the fault propagation network for the fault localization, finally, the Adaptive immune layer learns the unknown and incipient fault pattern. Layers interact with each other through activation signals and presenting antigen.The proposed MIFD draws its inspiration from variety of cells and different type of mechanisms in the natural immune system. It not only adopts multi-layered defense structure and works in tandem with each other, but also involves the self/non-self discrimination. The model considers the activation between clonal selection algorithm and immune network, utilizing the theory of primary response and B-lymphocyte secrete antibodies to deal with the unknown fault pattern. In order to implement fault diagnosis, we combine negative selection algorithm, clonal selection algorithm and immune network to build the framework for engineering artificial immune systems.The fault propagation layer adopts the structure of the B-lymphocyte network to construct the fault propagation model for the fault localization. In this structure, the cause-consequent relationship of fault propagation of systems corresponds to the interaction between B-lymphocytes in the immune system. In this model, with the network of B-lymphocytes representing the influence of fault node throughout system propagates and T-lymphocytes representing sensors loop. It realizes time-continuous fault diagnosis, incorporate the dynamics of the immune networks to the fault propagation model and exploit preliminary and precision processes diagnosis for the fault localization.In adaptive immune layer, we suggest a B-PCLONE learning algorithms which recognise the pattern of antigen by using double-learning mechanisms of B-lymphocyte and antibody. Through the continuous supplement and improvement of diagnostic knowledge, the system overcome limited size of learning sample and successfully make the system achieve optimal diagnostic results. In learning algorithms of antibody, we introduced Particle swarm optimization (PSO) into clonal selection algorithm and every candidate detector is regarded as a Particle. The algorithm used Particle optimization evolution equations to guide mutation direction of antibodies for global optimum corporately. Meanwhile, the consequent memory of the primary response of pathogen enables the immune system to mount a more rapid and efficient secondary response to similar faults.This thesis introduces the idea about the distinction between the function of B-lymphocyte and antibody. In the biological humoral immunity, the B- lymphocyte can secrete large numbers of antibodies to recognize and eliminate the antigens. Inspired by the relationship of B-lymphocyte and antibody, the detectors are defined as B-lymphocyte and antibodies which the B-lymphocyte produces. The fault is mapping to B-lymphocyte and the omens is mapping to antibodies. A same fault shows various omens represent as the B-lymphocyte produces antibodies. In a shape-space the various omens of a fault range at the fault. By using such a mechanism, it not only solves the problems that how to distinguish the faults which aroused by the overlap of the omens, but also improves the efficiency and accuracy of the fault detection and enhances the accuracy of continuous study.At last, experiments are undertaken to assess the effectiveness of the proposed model in an induction motor. The results of the detection show that the implemented MIFD can detect the antecedents to the faults. The effects of the continuous learning feature are demonstrated.